We are seeking an expert specializing in modern data architecture and cloud solutions to develop Snowflake-based "solution accelerators" for high-priority use cases, particularly focused on connected mobility and fleet management. The primary goal of this role is to create reusable, real-world codebases that can be quickly adapted for customer pilots, accelerating the sales and implementation journey. Overview: The contractor will be responsible for the hands-on coding and development required to transform core industry pain points into fully functional, easily deployable solutions. The resulting accelerators must be sophisticated and realistic to address complex challenges that current industry systems struggle to handle. This role prioritizes the construction of robust, efficient, and scalable data pipelines and applications. Key Responsibilities: Solution Development: Act as a hands-on developer to build production-ready solution accelerators, ensuring the codebase is reusable and adaptable for customer needs. Performance Optimization: Implement pipelines aimed at reducing latency, particularly transitioning micro-batch processing to a streaming architecture. This may involve leveraging Snowpark, SQL or Snowpipe Streaming. Documentation & Assets: Provide a comprehensive set of assets, including the GitHub code, architecture diagram and technical documentation, to enable field teams and partners (like system integrators) to run implementations and pilots. Required Technical Skills and Platform Expertise: The candidate must demonstrate deep expertise in leveraging the following aspects of the Snowflake platform: Real-time/Streaming Ingestion: Experience with Snowpipe Streaming to ingest high-volume telematics data streams for lowest latency. Application Hosting: Experience utilizing Snowpark Container Services (SPCS) to containerize services, such as implementing cost-effective, open-source routing engines. Visualization: Expertise in building front-end applications and dashboards using Stream lit to provide an actionable view of operations, including heatmaps, alerts, and KPI feeds. Data Science and Machine Learning: Working knowledge of applying ML models for modeling and analysis on top of the data. Advanced Analytics (nice to have): Mastery of Time-Series functions and Geospatial analytics, including H3 hexagons. Preferred Industry Expertise: It would be nice to have if the candidate is familiar with the following fleet and IoT intelligence use cases: • Predictive Maintenance: Developing solutions that integrate IoT sensor data (e.g., Diagnostic Trouble Codes, voltage, temperature) to predict asset failure and trigger automated work orders. • Fleet Efficiency and Optimization: Building capabilities such as: • Route Optimization: Comparing expected or optimal routes against actual driver behavior and flagging deviations for proactive alerting. • Inbound Logistics Management: Creating visibility solutions to prevent costly delays in Just-in-Time production by tracking inbound part deliveries. • Idling Reduction: Solutions that analyze vehicle telemetry (like battery charge and voltage) to identify unnecessary idling and send near real-time alerts to drivers to achieve fuel savings. • IoT Operations Intelligence: Developing solutions that handle core problems common to both moving and static assets (e.g., time-to-action and predictive maintenance) within a single, reusable architecture. Originally posted on Himalayas